This report investigates the critical research question: How do temperature and precipitation changes affect agricultural yields? Understanding these relationships is vital for several reasons:
Food Security: With global population projected to only increase, optimizing agricultural productivity is essential.
Climate Change Adaptation: Farmers and policymakers need data-driven insights to adapt to changing climate patterns.
Economic Stability: Agriculture contributes significantly to many nations’ GDPs and employment.
The analysis focuses on five key crops (wheat, rice, potatoes, cotton, and maize) across eight countries (Australia, Canada, Germany, France, India, Mexico, Poland, and the United States) from 1961-2021.
#install.packages("readxl")
#install.packages("skimr")
#install.packages("patchwork")
#install.packages("tseries")
#install.packages("forecast")
#install.packages("vars")
#install.packages("dynlm")
library(dynlm)
library(readxl)
library(data.table)
library(dplyr)
library(tidyr)
library(ggplot2)
library(skimr)
library(patchwork)
library(tseries)
library(forecast)
library(vars)
library(tseries)
cropurl <- "https://catalog.ourworldindata.org/explorers/agriculture/latest/crop_yields/crop_yields.csv"
crop_yields <- fread(cropurl)
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rainearly <- read_excel("Rain1950-2014.xlsx")
rainlate <- read_excel("Rain2015-2024.xlsx")
temp <- read_excel("TempCelsius.xlsx")
crop_yields
rainearly
rainlate
temp <- temp %>%
rename(country = Name)
temp
NA
precipitation <- merge(rainearly, rainlate, by = "code", all = FALSE)
colnames(precipitation) <- sub("-07", "", colnames(precipitation))
precipitation <- precipitation %>%
rename(country = name.x)
precipitation <- precipitation %>%
dplyr::select(-code)
precipitation[, as.character(1950:2024)] <- lapply(precipitation[, as.character(1950:2024)], as.integer) #Gotta include as.character cause R isn't smart and doesn't understand a number (grrrr)
precipitation
filtered_prec <- setNames(data.frame(t(precipitation[,-1])), precipitation[,1])
filtered_prec <- cbind(year = as.integer(rownames(filtered_prec)), filtered_prec)
Warning: NAs introduced by coercion
rownames(filtered_prec) <- NULL # Cool lil trick
filtered_prec[-1] <- lapply(filtered_prec[-1], as.integer)
Warning: NAs introduced by coercionWarning: NAs introduced by coercionWarning: NAs introduced by coercionWarning: NAs introduced by coercionWarning: NAs introduced by coercionWarning: NAs introduced by coercionWarning: NAs introduced by coercionWarning: NAs introduced by coercion
filtered_prec
NA
temp <- temp %>%
dplyr::select(-Code)
temp[, as.character(1901:2023)] <- lapply(temp[, as.character(1901:2023)], as.double)
temp
# This made it much easier, but wanted to keep my prior struggles up
country_names <- temp$country
filtered_temp <- as.data.frame(t(temp[, -1]))
colnames(filtered_temp) <- country_names
filtered_temp <- cbind(
year = as.integer(rownames(filtered_temp)),
filtered_temp)
rownames(filtered_temp) <- NULL
filtered_temp
NA
# Quick note to self, use dplyr because it seems select() has some issues with another package
wheat_data <- crop_yields %>%
dplyr::select(country, year, wheat_yield)
rice_data <- crop_yields %>%
dplyr::select(country, year, rice_yield)
potato_data <- crop_yields %>%
dplyr::select(country, year, potato_yield)
cotton_data <- crop_yields %>%
dplyr::select(country, year, cotton_yield)
maize_data <- crop_yields %>%
dplyr::select(country, year, maize_yield)
# It's Pivot Time (Insert Friends Meme)
wheat_data <- wheat_data %>%
pivot_wider(names_from = country, values_from = wheat_yield) %>%
dplyr::select('year','Australia', 'Canada', 'Germany', 'France', 'India', 'Mexico', 'Poland', 'United States')
rice_data <- rice_data %>%
pivot_wider(names_from = country, values_from = rice_yield) %>%
dplyr::select('year','Australia', 'Canada', 'Germany', 'France', 'India', 'Mexico', 'Poland', 'United States')
potato_data <- potato_data %>%
pivot_wider(names_from = country, values_from = potato_yield) %>%
dplyr::select('year','Australia', 'Canada', 'Germany', 'France', 'India', 'Mexico', 'Poland', 'United States')
cotton_data <- cotton_data %>%
pivot_wider(names_from = country, values_from = cotton_yield) %>%
dplyr::select('year','Australia', 'Canada', 'Germany', 'France', 'India', 'Mexico', 'Poland', 'United States')
maize_data <- maize_data %>%
pivot_wider(names_from = country, values_from = maize_yield) %>%
dplyr::select('year','Australia', 'Canada', 'Germany', 'France', 'India', 'Mexico', 'Poland', 'United States')
wheat_data
rice_data
potato_data
cotton_data
maize_data
filtered_temp # Measured in Celcius
filtered_prec # Measured in MM
wheat_data #Yields are measured in tonnes per hectare (Whatever tf that means)
rice_data #Yields are measured in tonnes per hectare
potato_data #Yields are measured in tonnes per hectare
cotton_data #Yields are measured in tonnes per hectare
maize_data #Yields are measured in tonnes per hectare
# After a few hours of testing I saw I NEEDED TO PIVOT AGAIN
prec_long <- filtered_prec %>%
pivot_longer(cols = -year, names_to = "country", values_to = "Precipitation") %>%
drop_na() %>%
filter(year >= 1960)
temp_long <- filtered_temp %>%
pivot_longer(cols = -year, names_to = "country", values_to = "Temperature") %>%
drop_na() %>%
filter(year >= 1960)
wheat_long <- wheat_data %>%
pivot_longer(cols = -year, names_to = "country", values_to = "Wheat_Yield") %>%
drop_na() %>%
filter(year >= 1960)
rice_long <- rice_data %>%
pivot_longer(cols = -year, names_to = "country", values_to = "Rice_Yield") %>%
drop_na() %>%
filter(year >= 1960)
potato_long <- potato_data %>%
pivot_longer(cols = -year, names_to = "country", values_to = "Potato_Yield") %>%
drop_na() %>%
filter(year >= 1960)
cotton_long <- cotton_data %>%
pivot_longer(cols = -year, names_to = "country", values_to = "Cotton_Yield") %>%
drop_na() %>%
filter(year >= 1960)
maize_long <- maize_data %>%
pivot_longer(cols = -year, names_to = "country", values_to = "Maize_Yield") %>%
drop_na() %>%
filter(year >= 1960)
The methodology employed several time series techniques:
Stationarity Testing: Augmented Dickey-Fuller tests to check for non-stationarity
Autocorrelation Analysis: ACF/PACF plots to identify temporal patterns
Cross-Correlation: CCF plots to examine lead-lag relationships
Dynamic Linear Models (DLM): Regression models incorporating lagged effects
summary(prec_long)
year country Precipitation
Min. :1960 Length:520 Min. : 411.0
1st Qu.:1976 Class :character 1st Qu.: 703.5
Median :1992 Mode :character Median : 807.5
Mean :1992 Mean : 806.4
3rd Qu.:2008 3rd Qu.: 913.2
Max. :2024 Max. :1229.0
summary(temp_long)
year country Temperature
Min. :1960 Length:512 Min. :-6.848
1st Qu.:1976 Class :character 1st Qu.: 8.408
Median :1992 Mode :character Median :15.360
Mean :1992 Mean :14.446
3rd Qu.:2007 3rd Qu.:23.095
Max. :2023 Max. :26.827
summary(wheat_long)
year country Wheat_Yield
Min. :1961 Length:504 Min. :0.7299
1st Qu.:1976 Class :character 1st Qu.:2.0356
Median :1992 Mode :character Median :2.9891
Mean :1992 Mean :3.4972
3rd Qu.:2008 3rd Qu.:4.5366
Max. :2023 Max. :8.6296
summary(rice_long)
year country Rice_Yield
Min. :1961 Length:315 Min. : 1.294
1st Qu.:1976 Class :character 1st Qu.: 3.487
Median :1992 Mode :character Median : 5.065
Mean :1992 Mean : 5.176
3rd Qu.:2008 3rd Qu.: 6.426
Max. :2023 Max. :11.055
summary(potato_long)
year country Potato_Yield
Min. :1961 Length:504 Min. : 6.248
1st Qu.:1976 Class :character 1st Qu.:17.319
Median :1992 Mode :character Median :23.778
Mean :1992 Mean :25.768
3rd Qu.:2008 3rd Qu.:34.795
Max. :2023 Max. :51.636
summary(cotton_long)
year country Cotton_Yield
Min. :1961 Length:252 Min. :0.3203
1st Qu.:1976 Class :character 1st Qu.:1.3259
Median :1992 Mode :character Median :2.0943
Mean :1992 Mean :2.3006
3rd Qu.:2008 3rd Qu.:3.0876
Max. :2023 Max. :6.4675
summary(maize_long)
year country Maize_Yield
Min. :1960 Length:506 Min. : 0.8999
1st Qu.:1976 Class :character 1st Qu.: 2.7127
Median :1992 Mode :character Median : 5.2289
Mean :1992 Mean : 5.2447
3rd Qu.:2008 3rd Qu.: 7.3452
Max. :2024 Max. :11.2533
CountryPrecipitation <- ggplot(prec_long, aes(x = year, y = as.numeric(Precipitation), color = country)) +
geom_line() +
labs(title = "Annual Precipitation by Country (1961-2021)",
x = "Year",
y = "Precipitation (mm)",
color = "Country") +
theme_minimal() +
scale_x_continuous(breaks = seq(1961, 2021, by = 5)) +
theme(legend.position = "bottom", axis.text.x = element_text(angle = 90))
CountryPrecipitation
CountryTemp <- ggplot(temp_long, aes(x = year, y = as.numeric(Temperature), color = country)) +
geom_line() +
labs(title = "Annual Temperature by Country (1900-2021)",
x = "Year",
y = "Temperature (Celcius)",
color = "Country") +
theme_minimal() +
scale_x_continuous(breaks = seq(1900, 2021, by = 5)) +
theme(legend.position = "bottom", axis.text.x = element_text(angle = 90))
CountryTemp
annualWheat <- ggplot(wheat_long, aes(x = year, y = as.numeric(Wheat_Yield), color = country)) +
geom_line() +
labs(title = "Annual Wheat Yield (1961-2021)",
x = "Year",
y = "Wheat_Yield (Tonnes per Hectare)",
color = "Country") +
theme_minimal() +
scale_x_continuous(breaks = seq(1961, 2021, by = 3)) +
theme(legend.position = "bottom", axis.text.x = element_text(angle = 90))
annualWheat
annualRice <- ggplot(rice_long, aes(x = year, y = as.numeric(Rice_Yield), color = country)) +
geom_line() +
labs(title = "Annual Rice Yield (1961-2021)",
x = "Year",
y = "Rice_Yield (Tonnes per Hectare)",
color = "Country") +
theme_minimal() +
scale_x_continuous(breaks = seq(1961, 2021, by = 3)) +
theme(legend.position = "bottom", axis.text.x = element_text(angle = 90))
annualRice
annualPotato <- ggplot(potato_long , aes(x = year, y = as.numeric(Potato_Yield), color = country)) +
geom_line() +
labs(title = "Annual Potato Yield (1961-2021)",
x = "Year",
y = "Potato_Yield (Tonnes per Hectare)",
color = "Country") +
theme_minimal() +
scale_x_continuous(breaks = seq(1961, 2021, by = 3)) +
theme(legend.position = "bottom", axis.text.x = element_text(angle = 90))
annualPotato
annualCotton <- ggplot(cotton_long , aes(x = year, y = as.numeric(Cotton_Yield), color = country)) +
geom_line() +
labs(title = "Annual Cotton Yield (1961-2021)",
x = "Year",
y = "Cotton_Yield (Tonnes per Hectare)",
color = "Country") +
theme_minimal() +
scale_x_continuous(breaks = seq(1961, 2021, by = 3)) +
theme(legend.position = "bottom", axis.text.x = element_text(angle = 90))
annualCotton
annualMaize <- ggplot(maize_long , aes(x = year, y = as.numeric(Maize_Yield), color = country)) +
geom_line() +
labs(title = "Annual Maize Yield (1961-2021)",
x = "Year",
y = "Maize_Yield (Tonnes per Hectare)",
color = "Country") +
theme_minimal() +
scale_x_continuous(breaks = seq(1961, 2021, by = 3)) +
theme(legend.position = "bottom", axis.text.x = element_text(angle = 90))
annualMaize
adf_temp <- adf.test(temp_long$Temperature, alternative = "stationary")
Warning: p-value smaller than printed p-value
adf_prec <- adf.test(prec_long$Precipitation, alternative = "stationary")
Warning: p-value smaller than printed p-value
adf_wheat <- adf.test(wheat_long$Wheat_Yield, alternative = "stationary")
adf_rice <- adf.test(rice_long$Rice_Yield, alternative = "stationary")
Warning: p-value smaller than printed p-value
adf_potato <- adf.test(potato_long$Potato_Yield, alternative = "stationary")
adf_cotton <- adf.test(cotton_long$Cotton_Yield, alternative = "stationary")
adf_maize <- adf.test(maize_long$Maize_Yield, alternative = "stationary")
Warning: p-value smaller than printed p-value
print(adf_temp)
Augmented Dickey-Fuller Test
data: temp_long$Temperature
Dickey-Fuller = -4.6739, Lag order = 7, p-value = 0.01
alternative hypothesis: stationary
print(adf_prec)
Augmented Dickey-Fuller Test
data: prec_long$Precipitation
Dickey-Fuller = -5.7696, Lag order = 8, p-value = 0.01
alternative hypothesis: stationary
print(adf_wheat)
Augmented Dickey-Fuller Test
data: wheat_long$Wheat_Yield
Dickey-Fuller = -3.7997, Lag order = 7, p-value = 0.01902
alternative hypothesis: stationary
print(adf_rice)
Augmented Dickey-Fuller Test
data: rice_long$Rice_Yield
Dickey-Fuller = -4.873, Lag order = 6, p-value = 0.01
alternative hypothesis: stationary
print(adf_potato) # Non-Stationary so it might have Trend or Seasonality
Augmented Dickey-Fuller Test
data: potato_long$Potato_Yield
Dickey-Fuller = -3.2133, Lag order = 7, p-value = 0.08565
alternative hypothesis: stationary
print(adf_cotton)
Augmented Dickey-Fuller Test
data: cotton_long$Cotton_Yield
Dickey-Fuller = -3.4698, Lag order = 6, p-value = 0.04617
alternative hypothesis: stationary
print(adf_maize)
Augmented Dickey-Fuller Test
data: maize_long$Maize_Yield
Dickey-Fuller = -5.0584, Lag order = 7, p-value = 0.01
alternative hypothesis: stationary
potato_long$Potato_Yield_diff <- c(NA, diff(potato_long$Potato_Yield))
potato_long_diff <- potato_long$Potato_Yield_diff[!is.na(potato_long$Potato_Yield_diff)]
# Potatos man, why aren't you stationary
adf.test(potato_long_diff, alternative = "stationary")
Warning: p-value smaller than printed p-value
Augmented Dickey-Fuller Test
data: potato_long_diff
Dickey-Fuller = -19.657, Lag order = 7, p-value = 0.01
alternative hypothesis: stationary
merged_data <- merge(wheat_long, temp_long, by = c("country", "year"))
merged_data <- merge(merged_data, prec_long, by = c("country", "year"))
merged_data <- merge(merged_data, rice_long, by = c("country", "year"))
merged_data <- merge(merged_data, potato_long, by = c("country", "year"))
merged_data <- merge(merged_data, cotton_long, by = c("country", "year"))
merged_data <- merge(merged_data, maize_long, by = c("country", "year"))
head(merged_data)
NA
# Thank goodness for the guy who invinted copy/paste
acf(temp_long$Temperature, main="ACF of Temperature")
pacf(temp_long$Temperature, main="PACF of Temperature")
acf(prec_long$Precipitation, main="ACF of Precipitation")
pacf(prec_long$Precipitation, main="PACF of Precipitation")
acf(wheat_long$Wheat_Yield, main="ACF of Wheat Yield")
pacf(wheat_long$Wheat_Yield, main="PACF of Wheat Yield")
acf(rice_long$Rice_Yield, main="ACF of Rice Yield")
pacf(rice_long$Rice_Yield, main="PACF of Rice Yield")
acf(potato_long$Potato_Yield, main="ACF of Potato Yield")
pacf(potato_long$Potato_Yield, main="PACF of Potato Yield")
acf(cotton_long$Cotton_Yield, main="ACF of Cotton Yield")
pacf(cotton_long$Cotton_Yield, main="PACF of Cotton Yield")
acf(maize_long$Maize_Yield, main="ACF of Maize Yield")
pacf(maize_long$Maize_Yield, main="PACF of Maize Yield")
NA
NA
ccf(temp_long$Temperature, wheat_long$Wheat_Yield, main="Cross-Correlation: Temperature vs Wheat Yield")
ccf(prec_long$Precipitation, wheat_long$Wheat_Yield, main="Cross-Correlation: Precipitation vs Wheat Yield")
ccf(temp_long$Temperature, rice_long$Rice_Yield, main="Cross-Correlation: Temperature vs Rice Yield")
ccf(prec_long$Precipitation, rice_long$Rice_Yield, main="Cross-Correlation: Precipitation vs Rice Yield")
ccf(temp_long$Temperature, potato_long$Potato_Yield, main="Cross-Correlation: Temperature vs Potato Yield")
ccf(prec_long$Precipitation, potato_long$Potato_Yield, main="Cross-Correlation: Precipitation vs Potato Yield")
ccf(temp_long$Temperature, cotton_long$Cotton_Yield, main="Cross-Correlation: Temperature vs Cotton Yield")
ccf(prec_long$Precipitation, cotton_long$Cotton_Yield, main="Cross-Correlation: Precipitation vs Cotton Yield")
ccf(temp_long$Temperature, maize_long$Maize_Yield, main="Cross-Correlation: Temperature vs Maize Yield")
ccf(prec_long$Precipitation, maize_long$Maize_Yield, main="Cross-Correlation: Precipitation vs Maize Yield")
NA
NA
NA
# 3 years of lagged variables cause I thought that was about right
merged_data$Temp_Lag1 <- lag(merged_data$Temperature, 1)
merged_data$Temp_Lag2 <- lag(merged_data$Temperature, 2)
merged_data$Temp_Lag3 <- lag(merged_data$Temperature, 3)
merged_data$Prec_Lag1 <- lag(merged_data$Precipitation, 1)
merged_data$Prec_Lag2 <- lag(merged_data$Precipitation, 2)
merged_data$Prec_Lag3 <- lag(merged_data$Precipitation, 3)
merged_data$Wheat_Yield_Lag1 <- lag(merged_data$Wheat_Yield, 1)
merged_data$Wheat_Yield_Lag2 <- lag(merged_data$Wheat_Yield, 2)
merged_data$Wheat_Yield_Lag3 <- lag(merged_data$Wheat_Yield, 3)
merged_data$Rice_Yield_Lag1 <- lag(merged_data$Rice_Yield, 1)
merged_data$Rice_Yield_Lag2 <- lag(merged_data$Rice_Yield, 2)
merged_data$Rice_Yield_Lag3 <- lag(merged_data$Rice_Yield, 3)
merged_data$Potato_Yield_Lag1 <- lag(merged_data$Potato_Yield, 1)
merged_data$Potato_Yield_Lag2 <- lag(merged_data$Potato_Yield, 2)
merged_data$Potato_Yield_Lag3 <- lag(merged_data$Potato_Yield, 3)
merged_data$Cotton_Yield_Lag1 <- lag(merged_data$Cotton_Yield, 1)
merged_data$Cotton_Yield_Lag2 <- lag(merged_data$Cotton_Yield, 2)
merged_data$Cotton_Yield_Lag3 <- lag(merged_data$Cotton_Yield, 3)
merged_data$Maize_Yield_Lag1 <- lag(merged_data$Maize_Yield, 1)
merged_data$Maize_Yield_Lag2 <- lag(merged_data$Maize_Yield, 2)
merged_data$Maize_Yield_Lag3 <- lag(merged_data$Maize_Yield, 3)
head(merged_data)
NA
NA
dlm_wheat <- dynlm(
Wheat_Yield ~ Temperature + Temp_Lag1 + Temp_Lag2 + Temp_Lag3 +
Precipitation + Prec_Lag1 + Prec_Lag2 + Prec_Lag3,
data = merged_data
)
dlm_rice <- dynlm(
Rice_Yield ~ Temperature + Temp_Lag1 + Temp_Lag2 + Temp_Lag3 +
Precipitation + Prec_Lag1 + Prec_Lag2 + Prec_Lag3,
data = merged_data
)
dlm_potato <- dynlm(
Potato_Yield ~ Temperature + Temp_Lag1 + Temp_Lag2 + Temp_Lag3 +
Precipitation + Prec_Lag1 + Prec_Lag2 + Prec_Lag3,
data = merged_data
)
dlm_cotton <- dynlm(
Cotton_Yield ~ Temperature + Temp_Lag1 + Temp_Lag2 + Temp_Lag3 +
Precipitation + Prec_Lag1 + Prec_Lag2 + Prec_Lag3,
data = merged_data
)
dlm_maize <- dynlm(
Maize_Yield ~ Temperature + Temp_Lag1 + Temp_Lag2 + Temp_Lag3 +
Precipitation + Prec_Lag1 + Prec_Lag2 + Prec_Lag3,
data = merged_data
)
crop_models <- list(
Wheat = dlm_wheat,
Rice = dlm_rice,
Potato = dlm_potato,
Cotton = dlm_cotton,
Maize = dlm_maize
)
lapply(crop_models, summary) # New tool I found, perty cool
$Wheat
Time series regression with "numeric" data:
Start = 1, End = 249
Call:
dynlm(formula = Wheat_Yield ~ Temperature + Temp_Lag1 + Temp_Lag2 +
Temp_Lag3 + Precipitation + Prec_Lag1 + Prec_Lag2 + Prec_Lag3,
data = merged_data)
Residuals:
Min 1Q Median 3Q Max
-2.2953 -0.7706 -0.2360 0.4696 3.5697
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.0618796 0.3348124 6.158 3.07e-09 ***
Temperature 0.0964357 0.0785426 1.228 0.221
Temp_Lag1 -0.0093102 0.1048677 -0.089 0.929
Temp_Lag2 -0.0046332 0.1049700 -0.044 0.965
Temp_Lag3 -0.0942129 0.0789282 -1.194 0.234
Precipitation -0.0010152 0.0013796 -0.736 0.463
Prec_Lag1 -0.0001120 0.0018142 -0.062 0.951
Prec_Lag2 0.0000327 0.0018138 0.018 0.986
Prec_Lag3 0.0021170 0.0013737 1.541 0.125
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.206 on 240 degrees of freedom
(3 observations deleted due to missingness)
Multiple R-squared: 0.06431, Adjusted R-squared: 0.03312
F-statistic: 2.062 on 8 and 240 DF, p-value: 0.04027
$Rice
Time series regression with "numeric" data:
Start = 1, End = 249
Call:
dynlm(formula = Rice_Yield ~ Temperature + Temp_Lag1 + Temp_Lag2 +
Temp_Lag3 + Precipitation + Prec_Lag1 + Prec_Lag2 + Prec_Lag3,
data = merged_data)
Residuals:
Min 1Q Median 3Q Max
-3.0057 -1.2067 -0.1251 1.2046 3.7339
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.286e+01 4.160e-01 30.908 < 2e-16 ***
Temperature 9.669e-02 9.759e-02 0.991 0.322777
Temp_Lag1 6.940e-03 1.303e-01 0.053 0.957568
Temp_Lag2 -2.493e-02 1.304e-01 -0.191 0.848578
Temp_Lag3 -1.806e-01 9.807e-02 -1.841 0.066812 .
Precipitation -6.115e-03 1.714e-03 -3.567 0.000435 ***
Prec_Lag1 -7.317e-04 2.254e-03 -0.325 0.745758
Prec_Lag2 7.942e-05 2.254e-03 0.035 0.971919
Prec_Lag3 -2.162e-04 1.707e-03 -0.127 0.899331
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.498 on 240 degrees of freedom
(3 observations deleted due to missingness)
Multiple R-squared: 0.6342, Adjusted R-squared: 0.622
F-statistic: 52.01 on 8 and 240 DF, p-value: < 2.2e-16
$Potato
Time series regression with "numeric" data:
Start = 1, End = 249
Call:
dynlm(formula = Potato_Yield ~ Temperature + Temp_Lag1 + Temp_Lag2 +
Temp_Lag3 + Precipitation + Prec_Lag1 + Prec_Lag2 + Prec_Lag3,
data = merged_data)
Residuals:
Min 1Q Median 3Q Max
-14.9978 -7.2648 0.5286 6.1801 17.5574
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 56.099981 2.356219 23.809 <2e-16 ***
Temperature 0.256441 0.552738 0.464 0.6431
Temp_Lag1 -0.055166 0.737999 -0.075 0.9405
Temp_Lag2 -0.102610 0.738719 -0.139 0.8896
Temp_Lag3 -1.125574 0.555452 -2.026 0.0438 *
Precipitation -0.014852 0.009708 -1.530 0.1274
Prec_Lag1 -0.003934 0.012768 -0.308 0.7583
Prec_Lag2 0.002514 0.012765 0.197 0.8440
Prec_Lag3 0.002145 0.009667 0.222 0.8246
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 8.485 on 240 degrees of freedom
(3 observations deleted due to missingness)
Multiple R-squared: 0.4521, Adjusted R-squared: 0.4338
F-statistic: 24.75 on 8 and 240 DF, p-value: < 2.2e-16
$Cotton
Time series regression with "numeric" data:
Start = 1, End = 249
Call:
dynlm(formula = Cotton_Yield ~ Temperature + Temp_Lag1 + Temp_Lag2 +
Temp_Lag3 + Precipitation + Prec_Lag1 + Prec_Lag2 + Prec_Lag3,
data = merged_data)
Residuals:
Min 1Q Median 3Q Max
-3.4586 -0.5915 -0.1147 0.4881 2.7095
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.8431207 0.2592249 18.683 < 2e-16 ***
Temperature 0.0864677 0.0608107 1.422 0.156349
Temp_Lag1 0.0128889 0.0811927 0.159 0.874004
Temp_Lag2 -0.0090443 0.0812719 -0.111 0.911484
Temp_Lag3 -0.0581364 0.0611093 -0.951 0.342383
Precipitation -0.0039967 0.0010681 -3.742 0.000229 ***
Prec_Lag1 -0.0005016 0.0014047 -0.357 0.721359
Prec_Lag2 0.0001337 0.0014043 0.095 0.924231
Prec_Lag3 0.0004851 0.0010636 0.456 0.648719
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9334 on 240 degrees of freedom
(3 observations deleted due to missingness)
Multiple R-squared: 0.5265, Adjusted R-squared: 0.5108
F-statistic: 33.36 on 8 and 240 DF, p-value: < 2.2e-16
$Maize
Time series regression with "numeric" data:
Start = 1, End = 249
Call:
dynlm(formula = Maize_Yield ~ Temperature + Temp_Lag1 + Temp_Lag2 +
Temp_Lag3 + Precipitation + Prec_Lag1 + Prec_Lag2 + Prec_Lag3,
data = merged_data)
Residuals:
Min 1Q Median 3Q Max
-3.7232 -1.2681 -0.1942 1.1919 4.4604
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.243e+01 4.891e-01 25.415 <2e-16 ***
Temperature -3.187e-02 1.147e-01 -0.278 0.7814
Temp_Lag1 -1.216e-02 1.532e-01 -0.079 0.9368
Temp_Lag2 -2.645e-02 1.534e-01 -0.172 0.8632
Temp_Lag3 -2.657e-01 1.153e-01 -2.304 0.0221 *
Precipitation -2.487e-03 2.015e-03 -1.234 0.2184
Prec_Lag1 1.439e-05 2.651e-03 0.005 0.9957
Prec_Lag2 -9.984e-05 2.650e-03 -0.038 0.9700
Prec_Lag3 1.536e-04 2.007e-03 0.077 0.9391
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.761 on 240 degrees of freedom
(3 observations deleted due to missingness)
Multiple R-squared: 0.613, Adjusted R-squared: 0.6001
F-statistic: 47.51 on 8 and 240 DF, p-value: < 2.2e-16
Crop Yields:
Wheat: Steady increase (2-4x higher yields since 1960)
Rice: Similar growth pattern, especially pronounced in India
Potatoes: More volatile with recent stagnation in some countries
Cotton: Significant increases in the US and India
Maize: Consistent growth across most countries
The cross-correlation analysis showed:
Temperature shows strongest present time and 1-year lag effects
Precipitation effects often appear with 1-2 year lags
Wheat and maize show the clearest climate relationships
The findings were all very small and minute, but still aplicable:
Current-year temperature has negative effects across all crops (Very Small)
1-year lagged temperature shows compensatory positive effects (Still Very Small)
Precipitation effects are positive (Small Once Again)
Wheat and maize models show strongest explanatory power (R^2 which is my favorite)
Temperature Effects:
Precipitation Timing:
Crop Vulnerability:
Adaptive Farming: Encourage crop rotation strategies that account for 1-2 year climate lag effects
Irrigation Investment: Prioritize water management in regions showing precipitation declines
Breeding Programs: Focus on developing temperature-resilient varieties, especially for potatoes (since they aren’t very temperature resilient)
TECHNOLOGY: There aren’t really accounting for technological improvements
Major Weather: Excludes extreme weather events
Incorporate monthly climate data
Add soil quality and irrigation variables
Analyze climate change scenario projections
This analysis demonstrates that climate variables indeed have significant lagged effects on agricultural productivity, with important variations across crop types. These findings can inform both short-term agricultural planning and long-term climate adaptation strategies. While not completely accurate, this analysis helps the reader and farmers understand the difference that planetary occurrences has on agricultural life.
set.seed(123)
filtered_data <- merged_data %>% filter(year >= 1990) %>% arrange(year)
n_rows <- nrow(filtered_data)
train_rows <- round(0.80 * n_rows)
train_data <- filtered_data %>% slice(1:train_rows)
test_data <- filtered_data %>% slice((train_rows + 1):n_rows)
model <- lm(Wheat_Yield ~ Temperature + Temp_Lag1 + Temp_Lag2 + Temp_Lag3 +
Precipitation + Prec_Lag1 + Prec_Lag2 + Prec_Lag3,
data = train_data)
summary(model)
Call:
lm(formula = Wheat_Yield ~ Temperature + Temp_Lag1 + Temp_Lag2 +
Temp_Lag3 + Precipitation + Prec_Lag1 + Prec_Lag2 + Prec_Lag3,
data = train_data)
Residuals:
Min 1Q Median 3Q Max
-1.7017 -0.7469 -0.3947 0.1829 2.6188
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.992e+00 4.734e-01 4.207 5.64e-05 ***
Temperature 1.453e-01 3.196e-01 0.455 0.650
Temp_Lag1 7.245e-02 3.425e-01 0.212 0.833
Temp_Lag2 -7.057e-03 3.372e-01 -0.021 0.983
Temp_Lag3 -2.165e-01 3.164e-01 -0.684 0.495
Precipitation 1.068e-03 5.463e-03 0.195 0.845
Prec_Lag1 -4.850e-04 5.529e-03 -0.088 0.930
Prec_Lag2 7.082e-04 5.556e-03 0.127 0.899
Prec_Lag3 7.149e-05 5.532e-03 0.013 0.990
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.137 on 100 degrees of freedom
Multiple R-squared: 0.0965, Adjusted R-squared: 0.02422
F-statistic: 1.335 on 8 and 100 DF, p-value: 0.235
predictions <- predict(model, newdata = test_data)
test_data$predictions <- predictions
ggplot(test_data) +
geom_line(aes(x = year, y = Wheat_Yield, color = "Actual"), linewidth = 1) +
geom_line(aes(x = year, y = predictions, color = "Predicted"), linewidth = 1) +
scale_color_manual(values = c("Actual" = "blue", "Predicted" = "red")) +
labs(title = "Actual vs Predicted Wheat Yield (Test Set)", x = "Year", y = "Wheat Yield (tons/ha)") +
theme_minimal() +
theme(legend.position = "bottom")
rmse <- sqrt(mean((test_data$Wheat_Yield - test_data$predictions)^2))
print(paste("RMSE: ", round(rmse, 2)))
[1] "RMSE: 1.44"
mae <- mean(abs(test_data$Wheat_Yield - test_data$predictions))
print(paste("MAE: ", round(mae, 2)))
[1] "MAE: 0.94"